Predicting software build errors

ABSTRACT

Systems and methods for predicting a software build error are described herein. In one example, a method includes detecting a plurality of changes in software. The method also includes identifying a plurality of change lists, wherein a change list is identified for each of the plurality of changes in the software. Additionally, the method includes identifying a characteristic for each change list in the plurality of change lists. Furthermore, the method includes calculating a plurality of probabilities based at least in part on the characteristic of each of the plurality of change lists, wherein each of the probabilities indicates the likelihood of one of the plurality of change lists creating the software build error. The method also includes reporting the plurality of probabilities of the software build error.

BACKGROUND

Software development can involve developing software code that is to betranslated into machine executable code. The translation of softwarecode written by developers into machine executable code can be referredto as a software build. During software development, errors encounteredduring the software build can increase the amount of time to developsoftware. For example, some organizations develop software with teams ofdevelopers. In some instances, one team of developers may wait to builda portion of a software application until a second team has built aseparate portion of the software application. If the second team ofdevelopers encounters a software build error, the first team ofdevelopers may be delayed in building a portion of the softwareapplication. Therefore, minimizing software build errors can prevent adelay in the software development process.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects described herein. This summary is not anextensive overview of the claimed subject matter. This summary is notintended to identify key or critical elements of the claimed subjectmatter nor delineate the scope of the claimed subject matter. Thissummary's sole purpose is to present some concepts of the claimedsubject matter in a simplified form as a prelude to the more detaileddescription that is presented later.

An embodiment provides a method for predicting software build errors.The method includes detecting a plurality of changes in software. Themethod also includes identifying a plurality of change lists, wherein achange list is identified for each of the plurality of changes in thesoftware. In addition, the method includes identifying a characteristicfor each change list in the plurality of change lists. Furthermore, themethod includes calculating a plurality of probabilities based at leastin part on the characteristic of each of the plurality of change lists,wherein each of the probabilities indicates the likelihood of one of theplurality of change lists creating the software build error. The methodalso includes reporting the plurality of probabilities of the softwarebuild error.

Another embodiment is a system for predicting software build errors. Thesystem includes a display device to display a plurality ofprobabilities, a processor to execute processor executable code, and astorage device that stores processor executable code. The system detectsa plurality of changes in software. The system also identifies aplurality of change lists, wherein a change list is identified for eachof the plurality of changes in the software. In addition, the systemidentifies a characteristic for each change list in the plurality ofchange lists. Furthermore, the system identifies a logistic regression.The system also uses the logistic regression to calculate the pluralityof probabilities based at least in part on the characteristic of each ofthe plurality of change lists, wherein each of the probabilitiesindicates the likelihood of one of the plurality of change listscreating the software build error. Additionally, the system reports theplurality of probabilities of the software build error.

Another embodiment provides one or more tangible computer-readablestorage media comprising a plurality of instructions. The instructionscause a processor to detect a plurality of changes in software andidentify a plurality of change lists, wherein a change list isidentified for each of the plurality of changes in the software. Theinstructions also cause a processor to identify a characteristic foreach change list in the plurality of change lists. Furthermore, theinstructions cause a processor to calculate a plurality of probabilitiesbased at least in part on the characteristic of each of the plurality ofchange lists, wherein each of the probabilities indicates the likelihoodof one of the plurality of change lists creating the software builderror. The instructions also cause the processor to report the pluralityof probabilities of the software build error.

BRIEF DESCRIPTION OF THE DRAWINGS

The following detailed description may be better understood byreferencing the accompanying drawings, which contain specific examplesof numerous features of the disclosed subject matter.

FIG. 1 is a block diagram of an example of a computing system thatpredicts software build errors;

FIG. 2 is a process flow diagram illustrating an example of a method forpredicting software build errors;

FIG. 3 is a block diagram illustrating an example of a predictionanalysis module used to predict software build errors;

FIG. 4 is a block diagram illustrating an example of a build breakmodule used to analyze software build errors;

FIG. 5 is a block diagram illustrating an example of an update moduleused to update the prediction analysis module; and

FIG. 6 is a block diagram illustrating an example of a tangible,computer-readable storage media that predicts software build errors.

DETAILED DESCRIPTION

Various methods for predicting software build errors have been developedto minimize delays associated with software build errors. Some methodsinclude collecting information regarding certain aspects of the softwarecode such as the number of lines of software code changed since the lastsoftware build. These methods may attempt to determine the likelihood ofa successful software build based on the collected information. However,many of these methods focus on information derived from the softwarecode rather than the process of building software and the actual changesthat have been made to the software. Other methods include identifying aset of variables that may identify a software build error. However, manyof these methods rely on decision trees that use a fixed set ofvariables to identify when a software build may fail.

The techniques described herein can predict a software build error basedon any suitable number of probabilities of a software build error. Insome embodiments, the techniques described herein can identify a seriesof changes since the last software build and calculate a probabilitythat each change may create a software build error. A software build canrefer to the state of building software, which includes compilingsoftware (also referred to herein as software code) into machineexecutable files and linking the machine executable files to form anapplication. A software build error can include an error in the softwarecode that prevents the software code from being compiled into anexecutable file or prevents the software code from being linked. Asoftware build error may prevent the software code from being translatedinto machine executable code, which may prevent the software code frombeing incorporated in an application.

As a preliminary matter, some of the figures describe concepts in thecontext of one or more structural components, referred to asfunctionalities, modules, features, elements, etc. The variouscomponents shown in the figures can be implemented in any manner, forexample, by software, hardware (e.g., discrete logic components, etc.),firmware, and so on, or any combination of these implementations. In oneembodiment, the various components may reflect the use of correspondingcomponents in an actual implementation. In other embodiments, any singlecomponent illustrated in the figures may be implemented by a number ofactual components. The depiction of any two or more separate componentsin the figures may reflect different functions performed by a singleactual component. FIG. 1, discussed below, provides details regardingone system that may be used to implement the functions shown in thefigures.

Other figures describe the concepts in flowchart form. In this form,certain operations are described as constituting distinct blocksperformed in a certain order. Such implementations are exemplary andnon-limiting. Certain blocks described herein can be grouped togetherand performed in a single operation, certain blocks can be broken apartinto plural component blocks, and certain blocks can be performed in anorder that differs from that which is illustrated herein, including aparallel manner of performing the blocks. The blocks shown in theflowcharts can be implemented by software, hardware, firmware, manualprocessing, and the like, or any combination of these implementations.As used herein, hardware may include computer systems, discrete logiccomponents, such as application specific integrated circuits (ASICs),and the like, as well as any combinations thereof.

As for terminology, the phrase “configured to” encompasses any way thatany kind of structural component can be constructed to perform anidentified operation. The structural component can be configured toperform an operation using software, hardware, firmware and the like, orany combinations thereof.

The term “logic” encompasses any functionality for performing a task.For instance, each operation illustrated in the flowcharts correspondsto logic for performing that operation. An operation can be performedusing software, hardware, firmware, etc., or any combinations thereof.

As utilized herein, terms “component,” “system,” “client” and the likeare intended to refer to a computer-related entity, either hardware,software (e.g., in execution), and/or firmware, or a combinationthereof. For example, a component can be a process running on aprocessor, an object, an executable, a program, a function, a library, asubroutine, and/or a computer or a combination of software and hardware.By way of illustration, both an application running on a server and theserver can be a component. One or more components can reside within aprocess and a component can be localized on one computer and/ordistributed between two or more computers.

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from any tangible,computer-readable device, or media.

Computer-readable storage media can include but are not limited tomagnetic storage devices (e.g., hard disk, floppy disk, and magneticstrips, among others), optical disks (e.g., compact disk (CD), anddigital versatile disk (DVD), among others), smart cards, and flashmemory devices (e.g., card, stick, and key drive, among others). Incontrast, computer-readable media generally (i.e., not necessarilystorage media) may additionally include communication media such astransmission media for wireless signals and the like.

FIG. 1 is a block diagram of an example of a computing system thatpredicts software build errors. The computing system 100 may be, forexample, a mobile phone, laptop computer, desktop computer, or tabletcomputer, among others. The computing system 100 may include a processor102 that is adapted to execute stored instructions, as well as a memorydevice 104 that stores instructions that are executable by the processor102. The processor 102 can be a single core processor, a multi-coreprocessor, a computing cluster, or any number of other configurations.The memory device 104 can include random access memory (e.g., SRAM,DRAM, zero capacitor RAM, SONOS, eDRAM, EDO RAM, DDR RAM, RRAM, PRAM,etc.), read only memory (e.g., Mask ROM, PROM, EPROM, EEPROM, etc.),flash memory, or any other suitable memory systems. The instructionsthat are executed by the processor 102 may be used to predict softwarebuild errors.

The processor 102 may be connected through a system bus 106 (e.g., PCI,ISA, PCI-Express, HyperTransport®, NuBus, etc.) to an input/output (I/O)device interface 108 adapted to connect the computing system 100 to oneor more I/O devices 110. The I/O devices 110 may include, for example, akeyboard, a gesture recognition input device, a voice recognitiondevice, and a pointing device, wherein the pointing device may include atouchpad or a touchscreen, among others. The I/O devices 110 may bebuilt-in components of the computing system 100, or may be devices thatare externally connected to the computing system 100.

The processor 102 may also be linked through the system bus 106 to adisplay interface 112 adapted to connect the computing system 100 to adisplay device 114. The display device 114 may include a display screenthat is a built-in component of the computing system 100. The displaydevice 114 may also include a computer monitor, television, orprojector, among others, that is externally connected to the computingsystem 100. A network interface card (NIC) 116 may also be adapted toconnect the computing system 100 through the system bus 106 to a network(not depicted). The network (not depicted) may be a wide area network(WAN), local area network (LAN), or the Internet, among others.

The storage 118 can include a hard drive, an optical drive, a USB flashdrive, an array of drives, or any combinations thereof. The storage 118may include a prediction analysis module 120, a build break module 122,and an update module 124. The prediction analysis module 120 can detectany number of changes to software code and predict the likelihood thesoftware code contains a software build error. The prediction analysismodule 120 can predict the likelihood of software code containing asoftware build error by calculating the probability that each change tothe software code may cause a software build error. The build breakmodule 122 can build the software and detect a software build error. Ifthe build break module 122 detects a software build error, the buildbreak module 122 may also detect the change to the software code thatcaused a software build error. The build break module 122 can send thechanges that cause software build errors to the update module 124. Theupdate module 124 can store historical information for software codechanges and corresponding build errors. The update module 124 canprovide the historical information to the prediction analysis module120, which allows the prediction analysis module 120 to calculateaccurate predictions of software build errors.

It is to be understood that the block diagram of FIG. 1 is not intendedto indicate that the computing system 100 is to include all of thecomponents shown in FIG. 1. Rather, the computing system 100 can includefewer or additional components not illustrated in FIG. 1 (e.g.,additional applications, additional memory devices, additional networkinterfaces, etc.). For example, the computing system 100 may include areporting module that can report software build information to a user,an application, or another hardware device, among others. Furthermore,any of the functionalities of the prediction analysis module 120, buildbreak module 122, or update module 124 may be partially, or entirely,implemented in hardware and/or in the processor 102. For example, thefunctionality may be implemented with an application specific integratedcircuit, in logic implemented in the processor 102, or in any otherdevice.

FIG. 2 is a process flow diagram illustrating an example of a method forpredicting software build errors. The method 200 can be implemented witha computing system, such as the computing system 100 of FIG. 1. Thecomputing system 100 may also include a prediction analysis module 120that can predict software build errors based on changes to software codeand historical information of previous software code changes andprevious software build results.

At block 202, the prediction analysis module 120 detects changes insoftware code. In one embodiment, the prediction analysis module 120 candetect the changes to the software code by comparing the software codeto a previous version of the software code. For example, the predictionanalysis module 120 may detect changes in two different versions ofsoftware code by identifying differences in the software code. In otherembodiments, the prediction analysis module 120 may detect changes insoftware code by identifying indicators in the software code thatcorrespond with the changes to the software code. For example, theprediction analysis module 120 may detect changes in software code basedon comments included in the software code that correspond with changesin the software code.

At block 204, the prediction analysis module 120 identifies a changelist for each change to the software code. In some embodiments, theprediction analysis module 120 may include multiple changes in eachchange list. For example, a developer may change several lines ofsoftware code, which the prediction analysis module 120 can include inone change list. In other embodiments, the prediction analysis module120 may identify a change list for each developer or each work session.For example, the prediction analysis module 120 may identify the changesmade to software code from a particular developer and store the changesin a change list. In other examples, the prediction analysis module 120may identify each work session that includes changes to the softwarecode and identify a change list for each work day for each developer.

At block 206, the prediction analysis module 120 identifies acharacteristic for each change list. A characteristic of the change listcan include any information associated with a change in software code.In some embodiments, the characteristic may include information derivedfrom the software code. For example, a characteristic of the change listmay include the number of modified software code files or the number ofmodified lines of software code, among others. In some embodiments, thecharacteristic may also include information derived from factors relatedto the change in software code. For example, a characteristic of thechange list may include a determination of the developer that made thechange to the software code, the projects affected by the change, adetermination of the computing system the developer used to compile thesoftware code, the number or names of individuals that reviewed thechange to the software code (also referred to herein as a reviewdetermination), the time the change was submitted (also referred toherein as a time determination), complexity metrics related to thechange in the software code, and dependencies based on the changedsoftware code, among others. The complexity metrics can include thenumber of characters in a line of software code, the number of nestedloops surrounding the line of software code, or any other factors thatindicate the complexity of software code.

Additional examples of characteristics of the change list may includeany activity performed by other developers on source code files, orlines of source code files that have been modified or are related to agiven change list (referred to herein as an activity determination). Acharacteristic of a change list may also include a representation of thechanges made by developers. For example, the changes made by developersmay include source code fragments that have been introduced, identifiersthat have been referenced, or any other descriptions of changes made tosource code (referred to herein as a change determination). Additionalcharacteristics may also include the state of the source code repositorythat a developer's computing system was synced to when the software wasbuilt on the developer's computing system (also referred to herein as adeveloper build determination), the tests that were executed on thedeveloper's computing system, and the projects that were included aspart of the software build. The prediction analysis module 120 canprovide more accurate predictions by considering any number ofcharacteristics that correspond with a change list.

At block 208, the prediction analysis module 120 calculates aprobability for each change in the software code. In some embodiments,the probability for each change can represent the likelihood the changein the software code may result in a software build error. Theprobability can be calculated using regression, such as logisticregression. For example, the prediction analysis module 120 can generatea coefficient for each characteristic related to a change list. Theprediction analysis module 120 can determine the coefficients based onhistorical data. For example, historical data may indicate that aparticular software developer has a 20% probability of causing asoftware build error. In this example, the prediction analysis modulemay assign a 20% value as a coefficient to the characteristic related tothe software developer.

In some embodiments, the prediction analysis module 120 can combine theprobabilities of a software build error. For example, the predictionanalysis module 120 may calculate the individual probabilities (P1, P2,. . . PN) for each change list to cause a software build error. Theprediction analysis module 120 can combine the probabilities with theEquation 1.

1−(1−P1)(1−P2) . . . (1−PN)=P(Error)   Eq(1)

In Equation 1, P1 through PN represent probabilities that a change listmay cause a software build error. The term P(Error) represents thelikelihood that a software build error may occur based on N changes tothe software code.

In some embodiments the prediction analysis module 120 may aggregate thecharacteristics of the individual change lists and calculate a combinedprobability of a software build error for a plurality of change lists.For example, the prediction analysis module 120 may detect the number ofchange lists in a software build. The prediction analysis module 120 mayalso detect any suitable number of aggregate values associated with thechange lists. For example, three change lists may indicate that 100,150, and 200 lines of source code have been changed. The number ofchanged lines of code may be aggregated by summation into a combinednumber of changed lines of code, or 450 changed lines of code in theprevious example. The software build may be assigned a higher risk ofcausing a software build error if previous software builds with morethan 400 changed lines of source code resulted in software build errorsIn other examples, the prediction analysis module 120 may aggregate thecharacteristics of change lists by detecting the aggregate maximumprobability of a software build error, the aggregate minimum probabilityof a software build error, the aggregate average probability of asoftware build error, the aggregate median probability of a softwarebuild error, the summation of probabilities of a software build error,aggregated percentiles of a software build error, or the standarddeviation of a probability of a software build error, among others.

At block 210, the prediction analysis module 120 reports theprobabilities that each change in software code may cause a softwarebuild error. In some embodiments, the prediction analysis module 120 canreport the probabilities for software build errors to users,applications, or other hardware devices, among others. For example, theprediction analysis module 120 may calculate the probability that achange to the software code may result in a software build error is 20%.In this example, the prediction analysis module 120 may report to a userthe 20% prediction of the likelihood the change may cause a softwarebuild error. The process ends at block 212.

The process flow diagram of FIG. 2 is not intended to indicate that thesteps of the method 200 are to be executed in any particular order, orthat all of the steps of the method 200 are to be included in everycase. In some examples, the changes in software code may be detectedincrementally. For example, the prediction analysis module 120 maygenerate a new change list for each change to software code andrecalculate the probabilities of a software build error. In otherexamples, the characteristics may vary as additional changes to softwarecode are detected. For example, the prediction analysis module 120 maydetect various characteristics, such as complexity metrics, or a changedetermination, among others, for an integrated development environment.The prediction analysis module 120 may then detect additionalcharacteristics after a developer has committed changes to softwarecode. Further, any number of additional steps may be included within themethod 200, depending on the specific application. In some embodiments,the prediction analysis module 120 may send the change lists to a buildbreak module 122, which can determine if a change to the software codecauses a software build error. The build break module 122 is discussedbelow in greater detail in relation to FIG. 4. In other embodiments, theprediction analysis module 120 may send the change lists to an updatemodule 124. The update module 124 can update historical datacorresponding to change lists and software build errors. The updatemodule 124 is discussed below in greater detail in relation to FIG. 5.

FIG. 3 is a block diagram illustrating an example of a predictionanalysis module used to predict software build errors. The predictionanalysis module 120 can be implemented in a computing system, such asthe computing system 100 of FIG. 1. In some embodiments, the predictionanalysis module 120 can include a feature extraction component 302, aprediction generator 304, a trigger component 308, and a reportingcomponent 306. The components of the prediction analysis module 120 canidentify and analyze the likelihood of software build errors.

In some embodiments, the prediction analysis module 120 can accept anysuitable number of change lists as input. As discussed above, a changelist can include any appropriate number of changes to software code. Theprediction analysis module 120 can send the change lists to a featureextraction component 302. The feature extraction component 302 candetermine any appropriate number of characteristics associated with achange in software code. For example, the feature extraction component302 may identify characteristics associated with each change list. Insome examples, the feature extraction component 302 can identifycharacteristics such as the number of modified software code files, thenumber of modified lines of software code, the developer that made thechange to the software code, the projects affected by the change, thecomputing system the developer used to compile the software code, thenumber of individuals that reviewed the change to the software code, thetime the change was submitted, complexity metrics related to the changein the software code, and dependencies based on the changed softwarecode, among others. The feature extraction component 302 can send thechange list and the corresponding characteristics to a predictiongenerator 304.

The prediction generator 304 can calculate probabilities for softwarebuild errors based on the characteristics of the change lists. Asdiscussed above in relation to FIG. 2, the probabilities for softwarebuild errors can be calculated using a type of regression or usingmachine learning models including but not limited to support vectormachines, Naïve Bayes, or decision trees, among others. In someembodiments, the probabilities for software build errors are calculatedbased on linear regression or on logistic regression. In otherembodiments, the probabilities can be combined to calculate a likelihoodof a software build error based on the combined probabilities for eachchange list causing a software build error. In some embodiments, theprediction generator 304 can send the probabilities of a software builderror to a reporting component 306 and a trigger component 308.

The reporting component 306 can provide feedback to a user, anapplication, or a hardware device using any suitable number of methods.In some embodiments, the feedback can include the probability that achange list may cause a software build error. The reporting component306 may provide feedback through a message sent to the display device, adialog box generated in IDE, an email notification, or a newsfeed, amongothers.

The trigger component 308 can initiate or request additional actionsbased on the probability of a software build error. For example, thetrigger component 308 may provide feedback to developers through thereporting component 306. The feedback may request additional review ofsoftware code that has a high probability of creating a software builderror. In some examples, the feedback can identify particularcharacteristics of a change list and the corresponding probability ofcreating a software build error. The feedback may recommend additionalreview of a particular change list, to build additional projects beforesubmitting the changes to source code, or to run additional tests priorto submission, among others. In some embodiments, the trigger component308 can also request additional check-in or quality logic gates duringthe build process. For example, the trigger component 308 may include abuild component 310 that compiles and links software code to formmachine executable applications. The trigger component 308 may instructthe build component 310 to include additional quality logic gates, whichmay prevent a software build error. The additional quality logic gatesmay also assist in determining the cause of a software build error.

In some embodiments, the trigger component 308 may also provideinstructions to the build component 310 that control a software build.For example, the trigger component 308 may instruct the build component310 to build software by grouping change lists together that share alow-risk of creating a software build error. The trigger component 308can identify change lists that have a low-risk of causing a softwarebuild error based on probabilities that each change list may cause asoftware build error. In some embodiments, the trigger component 308 mayalso instruct the build component 310 to build high-risk change listsprior to building low-risk change lists. The trigger component 308 canalso identify change lists that have a high-risk of causing a softwarebuild error based on probabilities that indicate a change list is likelyto cause a software build error. For example, the trigger component 308may send high-risk change lists to multiple computing systems, which canallow the high-risk change sets to be built in parallel. The triggercomponent 308 may provide faster feedback to a reporting component 306regarding high-risk change lists when the high-risk change lists arebuilt in parallel.

In some embodiments, the trigger component 308 can also send the changelist to an update module 312 if the build component 310 does not returna software build error. The update module 122 can store historical dataregarding change lists and the success or failure of software buildsbased on the change lists. In some embodiments, the trigger component308 may receive a single change list indicating that a single changelist incorporates the changes made to software code. The triggercomponent 308 may send the results of the build to an update module 122if the build succeeds or fails. The update module 122 can then updatethe historical data to reflect whether the change list caused a softwarebuild error. In other embodiments, the trigger component 308 can sendthe change list to a build break module 122 when the build component 310returns a software build error. The build break module 122 can identifythe change list that caused the software build error and providefeedback to developers. The build break module 122 is discussed ingreater detail below in relation to FIG. 4.

It is to be understood that the block diagram of FIG. 3 is not intendedto indicate that the prediction analysis module 120 is to include all ofthe components shown in FIG. 3. Rather, the prediction analysis module120 can include fewer or additional components not illustrated in FIG.3. For example, the prediction analysis module 120 may not include abuild component 310. Rather, the functionality of the build component310 may be implemented by a processor, or any other suitable hardwaredevice.

FIG. 4 is a block diagram illustrating an example of a build breakmodule used to analyze software build errors. The build break module 122can be implemented in a computing system, such as the computing system100 of FIG. 1. In some embodiments, the build break module 122 caninclude a filter component 402, a prediction component 404, a validationcomponent 406, a reporting component 408, and a version controlcomponent 410. The components of the build break module 122 can analyzesoftware build errors.

The build break module 122 can accept any suitable number of changes tosoftware code and build logs produced by a build component. The buildlogs can include information generated during the build process. Forexample, the build logs may include information that indicates certainportions of the software code that did not produce a software builderror. In some embodiments, the filter component 402 can exclude anychanges to the software code that are known not to be responsible forthe software build error. For example, the filter component 402 mayidentify any changes included in previous builds that did not result ina software build error. In other embodiments, the filter component 402may perform dynamic analysis based on historical data stored in anupdate module 122. The filter component 402 can send a set of candidatechange lists that may have caused the software build error to theprediction component 404.

The prediction component 404 can detect the likelihood that a changelist caused the software build error. As discussed above in relation toFIG. 2, the probabilities for software build errors can be calculatedusing any suitable type of regression, such as logistic or linearregression. In some embodiments, the prediction component 404 cancalculate probabilities that each change list caused a software builderror.

In some embodiments, the prediction component 404 can send the changelists and the probabilities that each change list caused a softwarebuild error to a validation component 406. The validation component 406can recreate the build with each change list. In some embodiments, thevalidation component 406 may first select the change lists with thehighest probabilities of causing a software build error. The validationcomponent can recreate the software build and determine if the changelist causes a software build error. The validation component 406 maythen select the change lists with the lower probabilities of causing asoftware build error. In other embodiments, the validation component 406may use a binary search or delta debugging to determine the change liststhat cause a software build error. The validation component 406 candetermine the change lists that cause software build errors and send thechange lists to the reporting component 408. The reporting component 408can send the change lists and the software build errors to a displaydevice, a dialog box generated in IDE, an email notification, or anewsfeed, among others.

The reporting component 408 can also send the change lists and thesoftware build errors to a version control component 410. The versioncontrol component 410 can remove any modifications to the software codethat results in a software build error. For example, the version controlcomponent 410 can remove any changes to software code that prevents thesoftware code from being compiled and linked into a machine executableapplication. The reporting component 408 can also send the change listsand the software build errors to the update module 122, which isdiscussed below in greater detail in relation to FIG. 5.

It is to be understood that the block diagram of FIG. 4 is not intendedto indicate that the build break module 122 is to include all of thecomponents shown in FIG. 4. Rather, the build break module 122 caninclude fewer or additional components not illustrated in FIG. 4. Forexample, the build break module 122 may not include a validationcomponent 406. Rather, the functionality of the validation component 406may be implemented by a processor, or any other suitable hardwaredevice.

FIG. 5 is a block diagram illustrating an example of an update moduleused to update the prediction analysis module. The update module 124 canbe implemented in a computing system, such as the computing system 100of FIG. 1. In some embodiments, the update module 124 can include afeature extraction component 502, a historical database 504, and anupdate predictor module 506.

In some embodiments, the update module 124 can detect change lists andbuild outcomes associated with the change lists. The update module 124can then use a feature extraction component 502 to extractcharacteristics related to the change lists. For example, the featureextraction component 502 may extract characteristics such as thedeveloper that made the change to the software code, the projectsaffected by the change, the computing system the developer used tocompile the software code, the number of individuals that reviewed thechange to the software code, or the time the change was submitted, amongothers.

The feature extraction component 502 can send the change lists, buildoutcomes, and characteristics to a historical database 504. Thehistorical database 504 can store change lists and characteristics ofchange lists in a table along with the build outcome. In someembodiments, the historical database 504 can send data to the predictionanalysis module 120, which allows the prediction analysis module 120 tocalculate accurate probabilities of the likelihood a change list maycause a software build error. In other embodiments, the historicaldatabase 504 can also send the change lists, the build outcomes and thecharacteristics of change lists to an update predictor module 506. Theupdate predictor module 506 can train a prediction model based onhistorical data and send the prediction model to the prediction analysismodule 120 when the prediction model has an accuracy above a threshold.

It is to be understood that the block diagram of FIG. 5 is not intendedto indicate that the update module 124 is to include all of thecomponents shown in FIG. 5. Rather, the update module 124 can includefewer or additional components not illustrated in FIG. 5. For example,the update module 124 may not include an update predictor module 506.Rather, the functionality of the update predictor module 506 may beimplemented by a processor, or any other suitable hardware device.

FIG. 6 is a block diagram showing a tangible, computer-readable storagemedia 600 that predicts software build errors. The tangible,computer-readable storage media 600 may be accessed by a processor 602over a computer bus 604. Furthermore, the tangible, computer-readablestorage media 600 may include code to direct the processor 602 toperform the steps of the current method.

The various software components discussed herein may be stored on thetangible, computer-readable storage media 600, as indicated in FIG. 6.For example, the tangible computer-readable storage media 600 caninclude a prediction analysis module 606, a build break module 608, andan update module 610. The prediction analysis module 606 can detect anynumber of changes to software code and predict the likelihood thesoftware code contains a software build error. The build break module608 can analyze software build errors to determine the likelihood achange to software code has caused a build break. The update module 610can store historical information for software code changes andcorresponding software build errors. The update module 610 can providethe historical information to the prediction analysis module 120, whichallows the prediction analysis module 120 to calculate accuratepredictions regarding software build errors.

It is to be understood that any number of additional software componentsnot shown in FIG. 6 may be included within the tangible,computer-readable storage media 600, depending on the specificapplication. Although the subject matter has been described in languagespecific to structural features and/or methods, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific structural features or methodsdescribed above. Rather, the specific structural features and methodsdescribed above are disclosed as example forms of implementing theclaims.

What is claimed is:
 1. A method for predicting a software build error,comprising: detecting a plurality of changes in software; identifying aplurality of change lists, wherein a change list is identified for eachof the plurality of changes in the software; identifying acharacteristic for each change list in the plurality of change lists;calculating a plurality of probabilities based at least in part on thecharacteristic of each of the plurality of change lists, wherein each ofthe probabilities indicates the likelihood of one of the plurality ofchange lists creating the software build error; and reporting theplurality of probabilities of the software build error.
 2. The method ofclaim 1, comprising: building the software; detecting the software builderror; determining a change from the plurality of changes in thesoftware that resulted in the software build error; and reporting thechange that resulted in the software build error.
 3. The method of claim2, comprising updating a prediction generator used for predicting thesoftware build error based on the change that resulted in the softwarebuild error.
 4. The method of claim 2, comprising removing the changethat resulted in the software build error from the software.
 5. Themethod of claim 1, wherein calculating the plurality of probabilitiescomprises calculating a regression that indicates the likelihood thateach of the plurality of probabilities is to result in the softwarebuild error.
 6. The method of claim 1, comprising: identifying aplurality of high-risk change lists based on the plurality ofprobabilities; sending each of the plurality of high risk change liststo a separate computing system with instructions to build the softwarebased on the high-risk change list; and detecting the high-risk changelists that cause the software build error.
 7. The method of claim 1,wherein the plurality of characteristics comprise a combination of acomplexity metric, a software developer determination, a computingsystem determination, a time determination, a review determination, anactivity determination, a developer build determination, and a changedetermination.
 8. A system for predicting a software build error,comprising: a display device to display a plurality of probabilities; aprocessor to execute processor executable code; a storage device thatstores processor executable code, wherein the processor executable code,when executed by the processor, causes the processor to: detect aplurality of changes in software; identify a plurality of change lists,wherein a change list is identified for each of the plurality of changesin the software; identify a characteristic for each change list in theplurality of change lists; identifying a regression; using theregression to calculate the plurality of probabilities based at least inpart on the characteristic of each of the plurality of change lists,wherein each of the probabilities indicates the likelihood of one of theplurality of change lists creating the software build error; report theplurality of probabilities of the software build error; and recommend anaction to reduce the probability of the software build error.
 9. Thesystem of claim 8, wherein the processor executable code causes theprocessor to: build the software; detect the software build error;determine a change from the plurality of changes in the software thatresulted in the software build error; and report the change thatresulted in the software build error.
 10. The system of claim 9, whereinthe processor executable code causes the processor to update aprediction generator used for predicting the software build error basedon the change that resulted in the software build error.
 11. The systemof claim 9, wherein the processor executable code causes the processorto remove the change that resulted in the software build error from thesoftware.
 12. The system of claim 8, wherein the processor executablecode causes the processor to calculate a regression that indicates thelikelihood that each of the plurality of probabilities is to result inthe software build error.
 13. The system of claim 8, wherein theprocessor executable code causes the processor to: identify a pluralityof high-risk change lists based on the plurality of probabilities; sendeach of the plurality of high risk change lists to a separate computingsystem with instructions to build the software based on the high-riskchange list; and detect the high-risk change lists that cause thesoftware build error.
 14. The system of claim 8, wherein the pluralityof characteristics comprise any combination of a complexity metric, asoftware developer determination, a computing system determination, atime determination, a review determination, an activity determination, adeveloper build determination, and a change determination.
 15. One ormore computer-readable storage media comprising a plurality ofinstructions that, when executed by a processor, cause the processor to:detect a plurality of changes in software; identify a plurality ofchange lists, wherein a change list is identified for each of theplurality of changes in the software; identify a characteristic for eachchange list in the plurality of change lists; calculate a plurality ofprobabilities based at least in part on the characteristic of each ofthe plurality of change lists, wherein each of the probabilitiesindicates the likelihood of one of the plurality of change listscreating the software build error; report the plurality of probabilitiesof the software build error; and recommend an action to reduce theprobability of the software build error.
 16. The one or morecomputer-readable storage media of claim 15, wherein the plurality ofinstructions, when executed by the processor, cause the processor to:build the software; detect the software build error; determine a changefrom the plurality of changes in the software that resulted in thesoftware build error; and report the change that resulted in thesoftware build error.
 17. The one or more computer-readable storagemedia of claim 16, wherein the plurality of instructions cause theprocessor to update a prediction generator used for predicting thesoftware build error based on the change that resulted in the softwarebuild error.
 18. The one or more computer-readable storage media ofclaim 16, wherein the plurality of instructions cause the processor toremove the change that resulted in the software build error from thesoftware.
 19. The one or more computer-readable storage media of claim15, wherein the plurality of instructions cause the processor tocalculate a regression that indicates the likelihood that each of theplurality of probabilities is to result in the software build error. 20.The one or more computer-readable storage media of claim 15, wherein theplurality of characteristics comprise any combination of a complexitymetric, a software developer determination, a computing systemdetermination, a time determination, a review determination, an activitydetermination, a developer build determination, and a changedetermination.